This study examines the adoption of conservation agriculture practices (CAPs) among smallholder farmers in Tanzania. It finds that the adoption of different CAPs is interdependent, with practices often adopted as complements or substitutes. The likelihood of adoption is influenced by factors like production risk, access to extension services and markets, social networks, land characteristics, and farm size. Promoting CAP adoption requires properly targeting practices based on agroecology and improving farmers' organizations, market linkages, education, and extension services.

1. Adoption of Conservation Agriculture
Practices (CAPs): Evidence of
Interdependence in Plot Level Farmer
Technology Choice from Rural Tanzania
Menale Kassie, Bekele Shiferaw,
Moti Jaleta, Frank Mmbando et al

2. Outline
• Introduction
• Objectives
• Novelty
• Methodology
• Data
• What we find (results)
• Policy implications

3. Introduction-1
• Declining soil fertility and food insecurity and poverty
are major challenges facing African policymakers today
• The adoption and use of conservation agriculture
practices (CAPs) can help overcome these development
challenges
• CAPs may offer multiple benefits. But despite
substantial initiatives to encourage farmers to invest in
CAPs, adoption rates are still low in many countries
in SS Africa (Jansen et al. 2006; Wollni et al. 2010;
Shiferaw et al. 2011)

4. Introduction-2
• Relatively little empirical work has been done to
examine the socioeconomic factors that influence the
joint adoption and diffusion of CAPs, especially
conservation tillage, organic fertilizers, legume
intercropping and legume rotations (Arellanes and
Lee 2003).
• Understanding the determinants of farmers’ choices
of CAPs can provide insights into developing
strategies for targeting innovations to accelerate
diffusion.

5. Objectives
• Determine the extent of
adoption of CAPs among
smallholder farmers in
SIMLESA project areas in
Tanzania
• Assess the interdependence
between adoption of
different CAPs at the
farm/plot level
• Identify land characteristics,
household attributes and
market and institutional
factors that determine farmer

6. Why we do this
• There is much less research on adoption of multiple
CAPs by the same household; little understanding of
complementarities and substitution when farmer
invest in alternative options.
• The effect of social networks, market linkages and
institutional variables is less understood:
– Market access and value chain linkages
– Social capital: kinship and local networks
– Government effectiveness in services provision
– Biotic and abiotic shocks

7. Contribution to existing research -2
• To the best of our knowledge, no other study has
comprehensively and rigorously analyzed the joint
adoption of SAPs in the ESA region. The existing
studies in Tanzania (e.g., Mbaga-Semgalawe and
Folmer 2000; Isham 2002; Tenge et al. 2004) assessed
the determinants of partial technology adoption
(fertilizer or SWC), which ignored complementarities
and/or substitution effects.
• There are limited adoption studies on conservation
tillage, manure use, legume intercropping and rotations
in Africa in general and in Tanzania in particular.

8. Methodology-1
• Jointly analyze the factors that facilitate or impede the
probability of adopting CAPs for smallholder farmers in
Tanzania
• Multivariate probit (MVP) model
– There exist household and field level inter-relationships
between adoption decisions involving various CAP’s
– The choice of technologies adopted more recently by farmers
may be partly depend on earlier technology choices --- path
dependence
– Farm households face technology decision alternatives that
may be adopted simultaneously and/or sequentially as
complements, substitutes, or supplements

9. Methodology-2
• Unlike the univarite probit model, MVP
captures this inter-relationship and path
dependence of adoption
• Assumes that the unobserved heterogeneity
that affects the adoption of one of the CAPs
may also affect the choice of other CAPs
• Error terms from binary adoption decisions can
be correlated

10. Data
• SIMLESA data (2010):
– 700 farm households
– 1,589 managed plots
– 88 villages
– 4 districts
• Data type: detail
household, plot and
village information
collected
• Farming system: maize-
legumes

11. Crop composition: % total cultivated plots
allocated to maize and legumes
Crops Karatu Mbulu Mvomero Kilosa Total
Maize 46.9 52.1 61.0 61.4 54.9
Haricot bean 26.6 47.3 14.0 14.4 26.6
Pigeonpea 26.2 0.0 16.6 12.4 13.6
Other legumes 0.4 0.6 8.4 11.7 5.0
Total 100.0 100.0 100.0 100.0 100.0
Total Plots 542.0 535.0 344.0 555.0

12. Results: descriptive statistics-1
• Definition of Variables and Descriptive
Statistics.docx
Adoption of CAPs in Tanzania Mean Std. Dev.
Legume intercropping Plots received legume intercropping (1 =
0.46 0.50
(LI) yes)
Conservation tillage (CT) Plots received conservation tillage (1 = yes) 0.11 0.31
Soil & water
Plots received SWC practice (1 = yes) 0.18 0.39
conservation (SWC)
Animal manure(AM) Plots received animal manure (1 = yes) 0.23 0.42
Improved seeds(IS) Plots received improved seeds (1 = yes) 0.67 0.47
Cereal legume Plots received legume crop rotations (1 =
0.17 0.37
rotations(CLR) yes)
Chemical fertilizer (CF) Plots received chemical fertilizer (1 = yes) 0.04 0.20

13. Results: descriptive statistics-2
Some explanatory variables
Explanatory variables Mean Std. Dev.
Tenure Plot ownership (1 = owned plot; 0 = rented in plot) 0.89 0.31
Relatives Number of relatives that a farmer have within a village 8.56 15.96
Connections Household has relative in leadership position (1 = yes) 0.26 0.44
Market links Number of traders that farmer knows (number) 5.69 7.11
Extension Farmers trust the skills of extension agents (1 = yes) 0.61 0.49
Pestsdisease Pests and disease risk for crops (1 = yes) 0.64 0.48
Salaried Household member has salaried employment (1 = yes) 0.14 0.35
Gender Gender of household head (1 = male) 0.88 0.33
Insurance Household can rely on govt during crop failure (1 = yes) 0.35 0.50
Rainfalindex Rainfall satisfaction index 0.37 0.33
Group Participation in farmer coops or association (1 = yes) 0.29 0.46

14.  L TC
SW
M
C anur
CR
F e
 LI
Empirical Results: Correlation
Coefficients for MVP Regression Equations (p-value
in parentheses)
SAPs Legume Conservation Manure Legume Fertilizer SWC
intercropping tillage rotation
Conservation
tillage 0.21(0.00)
Manure
0.35(0.00) 0.10(0.26)
Legume rotation
-0.3(0.00) -0.16(0.17) -0.39(0.00)
Fertilizer
-0.03(0.75) -0.24(0.10) -0.07(0.57) -0.15(0.31)
SWC 0.03(0.59) 0.36(0.00) 0.11(0.09) 0.01(0.91) -0.07(0.52)
-0.03
Seed 0.50(0.00) -0.02(0.81) 0.13(0.00) -0.17(0.00) 0.42(0.00) (0.59)

15. Results – CAP adoption factors
Expl variables Conservation tillage SWC Legume intercropping Legume rotations
Rainfall (---) (--) (+++) (+++)
Pest and disease risk (+++) (++)
Trusted extension (++) (+++)
Connections (+)
Group /social network (+++) (+++)
Relatives/kins (---)
Market linkage (---)
Perceived public insurance (+++) (---) (--)
Distance to market (---) (---)
Distance to extension (---)
Timely avail of fertilizer (+++) (++)
Salaried hh (yes=1) (---) (-)
Gender (male=1)
Age (++) (++)
Education (+)
Livestock assets (+)
Farm size (---) (---) (++)
Other assets (+++) (-)
HH income (+)
Owned plot (++) (+++)
Plot size (+++) (++) (+++) (---)
Distance to plot (+++) (++)
Moderately fertile (++)
Low fertility (+++) (--)
lModerate slope (++) (+) (--)
Steep slope (---) (++) (++)
Moderate depth (+)

16. Results: CAP adoption factors (2)
Animal Fertilizer Improved
Variable Manure seed
Rainfall
Pest and disease risk (+++) (--)
Trusted extension (+++)
Group /Network (+++) (+++)
Market linkage (+++) (+)
Distance to market (+++)
Time avail of fert
Salaried hh (yes=1) (--)
Farm size (+++)
Age (---)
Education (+)
Livestock assets (+++) (++) (+++)
Farm size (--) (---)
Other assets (++)
HH income (+++)
Owned plot (+++) (+++)
Plot size (+++) (+++)
Distance to plot (--) (+++)
Low fertility (++)
Gentle slope (+)
Steep slope (+++) (-)
Moderate depth (++)

17. District level effects
Districts Conservat Legume Legume SWC Animal Fertilizer Improved
ion tillage intercropping rotations manure seed
Mbulu (+++) (---) (+++) (-) (---)
Mvomero (---) (---) (---) (---) (+++) (---)
Kilosa (---) (---) (---) (---) (---) (---)
Reference is Karatu district

18. Effect of CAPs on Crop Production
Kolmogorov-Smirnov Statistics Test
SAP type Distribution
0.2444
Legume intercrop (LI)
(p = 0.000)*** 1
0.2474
Animal manure .8
Cumulative Probability
(p = 0.000)***
0.2762 .6
Improved seeds
(p = 0.000)*** .4
0.1471
Chemical fertilizer (CF) .2
(p = 0.317)
0
Soil and water 0.0615
0 1000 2000 3000
Net value of crop production
conservation (SWC) (p = 0.440)
Without legume intercrop With legume intercrop
Conservation tillage (CT) 0.1059 Figure 1. Impact of legume intercrop on net value of crop production(' 000 TSh/acre)
(p = 0.087)*
Legume crop rotation 0.0522
(LCR) (p = 0.636)

19. Empirical results: MVP reults-1
• Production risk: The probability of adoption of CT,
SWC and LI is more common in areas and/or years
where rainfall is unreliable (in terms of timelines,
amount, and distribution)
• Extension - The quality of extension positively
influence adoption of CT, SWC, and improved seeds.

20. Empirical results: MVP reults-2
• Markets -The probability of adoption of capital-
intensive practices: improved seeds and fertilizer,
increase with enhanced value chain linkages (through
links with traders).
• Rural institutions -Participation in rural institutions
(groups, networks) enhances adoption of CAPs (LI,
SWC, animal manure and fertilizer).
• Public insurance - expectation of public safety nets
seems to reduce legume intercropping but increase
SWC.
• Off-farm income seems to be negatively associated
with CAP investment (poverty or specialization

21. Empirical results: MVP reults-3
• Land tenure influences adoption of SWC, CT, &
animal manure, which is more common on owner-
cultivated plots than on rented in (or borrowed) plots.
• Labor - availability of family labor is positively
associated with adoption of manure in crop
production
• Livestock also has positive effect on adoption of
improved seeds, fertilizer and legume rotations.

22. Empirical results: MVP reults-4
• Farm equipment ownership has a positive and
significant effect on adoption of CT and fertilizer.
• Farm size - Households that own less land are more
likely to adopt CT, LI, fertilizer and improved seeds;
but households with more land practice legume
rotations.
• Plot characteristics are important determinants of
CAP choice. Example - farmers are unlikely to adopt
CT, SWC, LI and improved seed on small plots.
SWC common on poor soils with gentle/steep slopes .

23. Conclusion
• Plot level interactions are important in identifying
suitable CAP combinations for specific environments.
• Policies that properly target CAPs based on agro-
ecology and are aimed at organizing small-scale
farmers into associations, improving market linkages,
education, and enhancing skills of civil servants can
increase adoption.
• Economic benefits from CAPs vary – good practice to
identify options that offer relatively quick benefits to
farmers.
• Future analysis needs to examine the productivity, risk,
environmental and welfare implications to particular
CAPs and combinations of sustainable agricultural